Publishing Partner: Cambridge University Press CUP Extra Publisher Login
amazon logo
More Info

New from Oxford University Press!


May I Quote You on That?

By Stephen Spector

A guide to English grammar and usage for the twenty-first century, pairing grammar rules with interesting and humorous quotations from American popular culture.

New from Cambridge University Press!


The Cambridge Handbook of Endangered Languages

Edited By Peter K. Austin and Julia Sallabank

This book "examines the reasons behind the dramatic loss of linguistic diversity, why it matters, and what can be done to document and support endangered languages."

Academic Paper

Title: Inductive probabilistic taxonomy learning using singular value decomposition
Author: Francesca Fallucchi
Institution: Università degli Studi di Roma Tor Vergata
Author: Fabio Massimo Zanzotto
Institution: Università degli Studi di Roma - La Sapienza
Linguistic Field: Computational Linguistics
Abstract: Capturing word meaning is one of the challenges of natural language processing (NLP). Formal models of meaning, such as networks of words or concepts, are knowledge repositories used in a variety of applications. To be effectively used, these networks have to be large or, at least, adapted to specific domains. Learning word meaning from texts is then an active area of research. Lexico-syntactic pattern methods are one of the possible solutions. Yet, these models do not use structural properties of target semantic relations, e.g. transitivity, during learning. In this paper, we propose a novel lexico-syntactic pattern probabilistic method for learning taxonomies that explicitly models transitivity and naturally exploits vector space model techniques for reducing space dimensions. We define two probabilistic models: the direct probabilistic model and the induced probabilistic model. The first is directly estimated on observations over text collections. The second uses transitivity on the direct probabilistic model to induce probabilities of derived events. Within our probabilistic model, we also propose a novel way of using singular value decomposition as unsupervised method for feature selection in estimating direct probabilities. We empirically show that the induced probabilistic taxonomy learning model outperforms state-of-the-art probabilistic models and our unsupervised feature selection method improves performance.


This article appears IN Natural Language Engineering Vol. 17, Issue 1, which you can READ on Cambridge's site or on LINGUIST .

Add a new paper
Return to Academic Papers main page
Return to Directory of Linguists main page